In this post I’m going to explore the “right context” in more detail. Whilst it is true that “All men are created equal”, the same cannot be said of analysis problems. It is important to determine the context of the problem as this brings with it a set of constraints and implicit assumptions that the analyst must understand and consider as they perform the analysis.The key elements of decision context are the:

scope

complexity

timeliness

Scope – is it an operational decision for example analyzing customer purchases to determine customers with the potential profit; simulate supply chains to reduce overall inventory levels and order-to-delivery times? Or is it strategic in nature for example deciding whether to move into a new market or embark on a strategic acquisition? Strategic and operational decisions have a different set of characteristics:Strategic Decisions

Long Term

Historic data

Internal and External perspective

Enterprise-wide data focus (information politics)

Focus on analytics and interpretation/heuristics

Poor data/information quality medium/high impact on decision

Long term feedback loop – enterprise’s goals/mission statement

Operational Decisions

Short Term

Real-time data

Internal perspective

Business unit or function data focus

Focus on analytics; less focus on interpretation

Poor data/information quality – high impact on decision

Short term feedback loop – focus on operational metrics

Complexity – problem can increase in complexity quickly and expand the scope of an analysis that is quantitative and focused on operational data to one that relies on the judgment of the analyst. The key is to be cognizant that the analytics guides the decision making.

Take this sales forecast. The data shows that Q1/Q2 has seen steady growth. A simple “interpretation” might assume that Q3 would also show steady growth. But let’s assume that Q3 shows a marked drop in sales in – will Q4 follow Path A (a return to Q1/Q2 growth) or Path B (a continuation of Q3’s falling sales). To answer this question may require data outside that which is normally available from operational systems. For example, if this was a weather dependent product or service you may need weather data to see if there’s a correlation between sales and temperature. A more complex correlation might be an increase in fuel costs leading to a drop in toll road usage. In both examples the decision required an analysis of the operational data and judgment/intuition on the part of the analyst.The final aspect of context is the Timeliness of the data. I explored this aspect in this blog post – analysis when the business needs it. The traditional view of business intelligence is of operational systems feeding a data warehouse through an Extract, Transform and Load (ETL) data pipeline. Little has changed with this model since it was initially proposed in the 1980’s. Increasingly organizations are realizing that the retrospective view of data this model supports is not sufficient to meet the demands of companies that need to function at internet speed. Rather than following this one speed approach organizations should adopt a tiered approach tailored to the organization’s business requirements, competitive environment and customer demands.

In a document to be published in early June and a series of TeleBriefings on June 2 and 3, Burton Group Senior Analyst Marcus Collins will explore the analysis context and each of the other critical success factors in more detail and provide guidance on how organizations can develop a roadmap for the successful deployment of a fact-based decision making culture.

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Business intelligence involves different strategies. The BI task can be handled with the aid of application software. Application software is broadly categorized under class of computer software that enables a computer to function in accordance with what the user desires.